Identification of control chart patterns using wavelet filtering and robust fuzzy clustering

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

49 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)343-350
Journal / PublicationJournal of Intelligent Manufacturing
Volume18
Issue number3
Publication statusPublished - Jun 2007
Externally publishedYes

Abstract

This paper proposes a hybrid framework composed of filtering module and clustering module to identify six common types of control chart patterns, including natural pattern, cyclic pattern, upward shift, downward shift, upward trend, and downward trend. In particular, a multi-scale wavelet filter is designed for denoising and its performance is compared to single-scale filters, including mean filter and exponentially weighted moving average (EWMA) filter. Moreover, three fuzzy clustering algorithms, based on fuzzy c means (FCM), entropy fuzzy c means (EFCM) and kernel fuzzy c means (KFCM), are adopted to compare their performance of pattern classification. Experimental results demonstrate that the excellent performance of EFCM and KFCM against outliers, especially in the case of high noise level embedded in the input data. Therefore, a hybrid framework combining wavelet filter with robust fuzzy clustering is suggested and proposed in this paper. Compared to neural network based approaches, the proposed method provides a promising way for the on-line recognition of control chart patterns because of its efficient computation and robustness against outliers. © 2007 Springer Science+Business Media, LLC.

Research Area(s)

  • Control chart, Outlier, Pattern recognition, Robust fuzzy clustering, Wavelet denoising